Weaviate vs Chroma
Weaviate
Enterprise-ready, distributed vector database with GraphQL API, advanced filtering, and multi-modal search capabilities.
Enterprise teams building large-scale RAG systems, multi-user SaaS platforms, and applications requiring fine-grained access control and complex filtering logic.
Chroma
Lightweight, open-source vector database optimized for Python-first RAG and embedding search workflows.
Indie developers, students, and teams rapidly prototyping RAG systems, chatbots, and semantic search features with manageable dataset sizes.
Short Answer
Weaviate is an enterprise-focused vector database with advanced filtering, multi-tenancy, and production scaling capabilities, while Chroma is a lightweight, developer-friendly vector database optimized for rapid prototyping and small-to-medium RAG applications. Weaviate suits complex deployments; Chroma excels for quick integration.
Our Verdict
AI-assistedChoose Weaviate if you need enterprise-grade features like hybrid search, multi-tenancy, complex filtering, and the ability to scale to 100M+ vectors in production environments. Choose Chroma if you're building quick prototypes, learning RAG applications, or need a lightweight in-process vector store that gets you running in minutes without infrastructure overhead.
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Choose Weaviate if
Enterprise teams building large-scale RAG systems, multi-user SaaS platforms, and applications requiring fine-grained access control and complex filtering logic.
Choose Chroma if
Indie developers, students, and teams rapidly prototyping RAG systems, chatbots, and semantic search features with manageable dataset sizes.
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Key Differences at a Glance
Key Facts & Figures
| Metric | Weaviate | Chroma | Diff |
|---|---|---|---|
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | — | — |
| Time to First Query(minutes) | 30-45 minutes (self-hosted) | 5 minutes | +660% |
| Maximum Vector Dimensions(dimensions) | Unlimited | 65,536 | — |
| Query Latency (p99)(milliseconds) | 50-150ms | 50-200ms | -20% |
| Indexing Methods Supported(count) | 3 methods (HNSW, flat, dynamic) | — | — |
| Average Query Latency (1M vectors, 384-dim)(milliseconds) | 75ms | — | — |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | — | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | — | — |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | — | — |
| API Query Language Support(count) | 2 (GraphQL, REST) | — | — |
| Query Throughput(operations per second (QPS)) | 100,000 QPS | — | — |
| Maximum Collection Size(billion vectors) | 2 billion vectors | — | — |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | — | — |
| GitHub Community Stars(stars) | 13,000+ stars | — | — |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers | — | — |
| Query Latency (95th percentile)(milliseconds) | 100-500 ms | — | — |
| Memory per 1M Vectors(GB) | 8-12 GB | — | — |
| Startup Time (empty instance)(seconds) | 20-30 seconds | — | — |
| Built-in LLM Integrations(count) | 15+ providers | — | — |
| Managed Cloud Base Price (monthly)(USD) | $25/month | — | — |
| Throughput (vectors/second insert)(vectors/sec) | 5,000-10,000 | — | — |
| Maximum Vectors Per Instance(vectors) | 100M+ (distributed) | ~10M | +900% |
| Average Query Latency(milliseconds) | 50-150ms | 10-50ms | +233% |
| Setup Time to First Query(minutes) | 30-60 (with Docker) | 2-5 (pip install) | +1400% |
| GitHub Stars | ~9,500 stars (as of 2026) | ~15,000 stars (as of 2026) | -37% |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | 1-2GB | +300% |
| Setup Time (First Query)(minutes) | 30-60 minutes | 2-5 minutes | +1400% |
| Max Recommended Vector Count(vectors) | 100M+ (distributed) | 1-10M (single node) | +900% |
| Monthly Starting Cost(USD) | $0 (free, open-source) | $0 (free, open-source) | — |
| Maximum Vector Storage(Vectors) | ~10M (single instance practical limit) | ~10M (single instance practical limit) | — |
| Setup Time (Local Development)(Minutes) | 2-5 (pip install + Python) | 2-5 (pip install + Python) | — |
| Cost at 10M Vectors/Month(USD) | $0 (self-hosted only) | $0 (self-hosted only) | — |
| Starting Cost (Annual)(USD) | $0 (free) | $0 (free) | — |
| Maximum Vectors at Scale(millions) | Limited to hardware (~1B) | Limited to hardware (~1B) | — |
| Query Latency (p95)(milliseconds) | 50-200ms local | 50-200ms local | — |
| Documentation Quality Score(out of 10) | 8/10 | 8/10 | — |
| Metadata Filter Complexity(operators supported) | Basic ($where) | Basic ($where) | — |
| Setup Time to Production(days) | 0.1 days (2-4 hours) | 0.1 days (2-4 hours) | — |
| Maximum Vector Scale(vectors) | ~10 million efficiently | ~10 million efficiently | — |
| Query Latency (1M vectors)(milliseconds) | 50-200ms | 50-200ms | — |
| Memory Usage (10M vectors)(GB) | 3-5 GB | 3-5 GB | — |
| Query Latency (1M vectors, single query)(milliseconds) | 150-300ms | 150-300ms | — |
| Maximum Practical Dataset Size(vectors) | ~10 million | ~10 million | — |
| Data Connectors(connectors) | 0 (manual) | 0 (manual) | — |
| LLM Provider Support(providers) | External (0 native) | External (0 native) | — |
| Minimum Deployment Size(megabytes) | 50 | 50 | — |
| Retrieval Strategy Types(strategies) | 1 (similarity search) | 1 (similarity search) | — |
| Storage Backends(backend types) | 3 (in-memory, SQLite, cloud) | 3 (in-memory, SQLite, cloud) | — |
| Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds) | ~50ms | ~50ms | — |
| GitHub Stars (as of 2026)(stars) | ~14,000 | ~14,000 | — |
| Memory Footprint (at rest, 1M vectors)(MB) | ~800MB | ~800MB | — |
| Number of Supported Languages(languages) | Python + JavaScript | Python + JavaScript | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
Weaviate
Enterprise-scale vector search with hybrid capabilities
Chroma
Fast prototyping and lightweight embeddings
Weaviate
Advanced filtering with BM25 + vector hybrid search🏆
Chroma
Basic filtering and metadata filtering only
Weaviate
Native multi-tenancy with tenant isolation🏆
Chroma
No native multi-tenancy support
Weaviate
Requires Docker/Kubernetes, moderate DevOps overhead
Chroma
In-memory or persistent, runs in-process, minimal setup🏆
Weaviate
50-150ms average query latency
Chroma
10-50ms average query latency🏆
Weaviate
Handles 100M+ vectors in distributed clusters🏆
Chroma
Practical limit ~10M vectors per instance
Weaviate
9,200+ stars on GitHub
Chroma
13,000+ stars on GitHub🏆
Full Comparison
| Attribute | Weaviate | Chroma |
|---|---|---|
| Free Tier Vector Limit(vectors) | Unlimited (self-hosted) | — |
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | — |
| Time to First Query(minutes) | 30-45 minutes (self-hosted) | 5 minutes |
| Maximum Vector Dimensions(dimensions) | Unlimited | 65,536 |
| Query Latency (p99)(milliseconds) | 50-150ms | 50-200ms |
| Indexing Methods Supported(count) | 3 methods (HNSW, flat, dynamic) | — |
| Average Query Latency (1M vectors, 384-dim)(milliseconds) | 75ms | — |
| Query Throughput(operations per second (QPS)) | 100,000 QPS | — |
| GPU Acceleration Support | Limited (planning phase) | — |
Show 8 more attributesQuery Latency (95th percentile)(milliseconds) 100-500 ms — Throughput (vectors/second insert)(vectors/sec) 5,000-10,000 — Average Query Latency(milliseconds) 50-150ms 10-50ms Query Latency (p95)(milliseconds) 50-200ms local — Query Latency (1M vectors)(milliseconds) 50-200ms — Query Latency (1M vectors, single query)(milliseconds) 150-300ms — Minimum Deployment Size(megabytes) 50 — Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds) ~50ms — | ||
| Uptime SLA(percent) | Not guaranteed (self-hosted) | None (community-supported) |
| Uptime Guarantee(percent) | No SLA | — |
| Native Hybrid Search Support(null) | BM25 keyword + vector | — |
| Built-in Hybrid Search Support | Native BM25 + vector search | — |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers | — |
| Hybrid Search Support (BM25 + Vector) | Yes | No |
| Multi-tenancy Support | Native with isolation | Not supported |
Show 9 more attributesQuery Filtering Support Advanced GraphQL + WHERE clauses with boolean logic Basic metadata filters Multi-Modal Search Text, image, audio, video Text embeddings only Metadata Filter Complexity(operators supported) Basic ($where) — Embedded Tokenizer Support Yes (6+ models included) — Metadata Filtering Support Native (boolean operators) — Data Connectors(connectors) 0 (manual) — Retrieval Strategy Types(strategies) 1 (similarity search) — Storage Backends(backend types) 3 (in-memory, SQLite, cloud) — Built-in Embedding Generation Yes (OpenAI, HuggingFace, Ollama) — | ||
| Deployment Model | Cloud-managed SaaS + Self-hosted Docker/Kubernetes | — |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | — |
| Built-in LLM Integrations(count) | 15+ providers | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | — |
| Licensing Cost(USD) | $0-5000+/month (SaaS) | — |
| Native Multi-tenancy Support | Yes, with built-in tenant isolation | — |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | — |
| Maximum Collection Size(billion vectors) | 2 billion vectors | — |
| Maximum Vectors Per Instance(vectors) | 100M+ (distributed) | ~10M |
| Max Recommended Vector Count(vectors) | 100M+ (distributed) | 1-10M (single node) |
| Maximum Vector Storage(Vectors) | ~10M (single instance practical limit) | — |
Show 3 more attributesMaximum Vectors at Scale(millions) Limited to hardware (~1B) — Maximum Vector Scale(vectors) ~10 million efficiently — Maximum Practical Dataset Size(vectors) ~10 million — | ||
| API Query Language Support(count) | 2 (GraphQL, REST) | — |
| Setup Time (First Query)(minutes) | 30-60 minutes | 2-5 minutes |
| Setup Time to Production(days) | 0.1 days (2-4 hours) | — |
| Setup Time(minutes) | 5 | — |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | — |
| Setup Time to First Query(minutes) | 30-60 (with Docker) | 2-5 (pip install) |
| Setup Time (Local Development)(Minutes) | 2-5 (pip install + Python) | — |
| GitHub Community Stars(stars) | 13,000+ stars | — |
| GitHub Stars (as of 2026)(stars) | ~14,000 | — |
| Memory per 1M Vectors(GB) | 8-12 GB | — |
| Memory Footprint (at rest, 1M vectors)(MB) | ~800MB | — |
| Startup Time (empty instance)(seconds) | 20-30 seconds | — |
| Supported Deployment Modes | Docker, Kubernetes, Cloud (AWS/GCP/Azure) | In-process, SQLite, HTTP API |
| Minimum Setup Infrastructure | Docker/Kubernetes cluster (4GB+ RAM minimum) | Python 3.7+; runs on laptop or serverless |
| Managed Cloud Base Price (monthly)(USD) | $25/month | — |
| Monthly Starting Cost(USD) | $0 (free, open-source) | — |
| Cost at 10M Vectors/Month(USD) | $0 (self-hosted only) | — |
| Starting Cost (Annual)(USD) | $0 (free) | — |
| Multi-modal Support (native)(modalities) | 3 (text, image, audio) | — |
| GitHub Stars | ~9,500 stars (as of 2026) | ~15,000 stars (as of 2026) |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | 1-2GB |
| Kubernetes Support | Native Kubernetes-ready Helm charts | Not native; runs as Python process |
| LangChain Integration Maturity | Supported but secondary to GraphQL API | Official, first-class integration |
| Documentation Quality Score(out of 10) | 8/10 | — |
| GPU Support | Experimental/Limited | — |
| Memory Usage (10M vectors)(GB) | 3-5 GB | — |
| LLM Provider Support(providers) | External (0 native) | — |
| Production Observability(feature count) | Basic logging | — |
| Kubernetes-Native Deployment | Not recommended; in-process only | — |
| Installation Complexity(minutes) | 5-10 minutes (Python package) | — |
| SQL Filtering Capability | JSON metadata filters (limited) | — |
| Open Source License | Apache 2.0 (fully open) | — |
| Supported Index Types(count) | Heuristic Search Algorithm (HNSW) | — |
| Number of Supported Languages(languages) | Python + JavaScript | — |
| Complex Metadata Filtering Support | Basic equality/contains only | — |
Show 8 more attributes
Show 9 more attributes
Show 3 more attributes
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
Weaviate
Pros
- Hybrid BM25 + vector search combining keyword and semantic relevance
- Native multi-tenancy with isolated data per tenant
- Advanced filtering with WHERE clauses supporting complex predicates
- Distributed architecture scales to 100M+ vectors across clusters
- GraphQL and REST APIs with rich querying capabilities
Cons
- Steeper learning curve with multiple configuration options
- Requires Docker/Kubernetes for production deployments, increasing operational complexity
- Higher memory footprint compared to lightweight alternatives
Chroma
Pros
- Minimal setup—runs in-process or with simple persistent storage without containers
- Fastest query latency (10-50ms) for small-to-medium datasets
- Seamless integration with LangChain and LLamaIndex frameworks
- Simple metadata filtering suitable for common use cases
- Excellent documentation and active community (13K+ GitHub stars)
Cons
- Cannot scale beyond ~10M vectors per instance, unsuitable for enterprise scale
- Lacks hybrid search—pure vector similarity only, no keyword matching
- No multi-tenancy, requiring separate instances for data isolation
Frequently Asked Questions
Choose Weaviate for production systems requiring 10M+ vectors, multi-user access, complex filtering, or hybrid search combining keywords with semantic similarity. Choose Chroma only if your dataset stays under 10M vectors and you don't need multi-tenancy or keyword search—Chroma's lightweight nature makes it excellent for single-user or small-team applications.
Resources & Learn More
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Where to Buy
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